A Brief Introduction to Snakemake

Eric C. Anderson

Computational Methods for Molecular Biology, SWFSC/CSU

What the Heck is Snakemake?

  • A Python-based “Workflow Management System”
  • Allows you to define a complex (bioinformatic) workflow as a series of steps that involve input files and output files.
  • It identifies the dependencies between the steps and then runs all the steps needed to create a requested output file.
  • This greatly simplifies the orchestration of bioinformatics, and makes it much easier to find and re-run failed jobs.
  • Incredibly valuable for reproducible research:
    • Not just so others can reproduce your results
    • Also useful for you to quickly run your workflow on different clusters, etc.

That sounds pretty jargony!

  • Illustrate with an example
  • Hope that it piques the curiosity of some

Our Small Example: GATK Best Practices “Light”

flowchart TD
  A(fastq files from 3 samples: our raw data) --> B(Trim the reads: fastp)
  B --> C(Map the reads to a reference genome: bwa-mem2)
  C --> D(Mark PCR and optical duplicates: gatk MarkDuplicates)
  D --> E(Make gVCF files for each sample/chromo: gatk HaplotypeCaller)
  E --> F(Load gVCFs into Genomic DB for each chromo: gatk GenomicsDBImport)
  F --> G(Create VCFs from Genomic DB for each chromo: gatk GenotypeGVCFs)
  G --> H(Concatenate chromosome-vcfs into a single vcf: bcftools)

A mini data set that only takes about 5 minutes to run through the major steps of a GATK-like variant calling workflow

  • Chinook salmon sequencing reads (a subset of our course example data).
  • Three paired-end fastqs from samples A, B, and C and data only from four chromosomes.
  • We will trim it, map it, mark duplicates, then make one gVCF file for each combination of individual and chromosome (only two chromosomes).
  • Then, call variants on each of two chromosomes.
  • Then catenate the resulting VCFs into a single VCF file.

Setting up our workspaces

  • Sync your fork’s main branch then pull updates into the main branch of your local clone.
  • On Alpine, get 4 cores on acompile.
  • Then, we simply need to ensure that we have a recent version of Snakemake.
  • We will create a mamba/conda environment snakemake-8.5.3 that has the latest version of snakemake as of this writing.
  • Once created, we activate that environment.
  • cd to the Snakemake-Example data directory inside the con-gen-csu repo.
# get onto acompile with 4 cores for interactive work
# (Or do what it takes to get a compute node with 4 cores on your own cluster)
acompile -n 4

# Create snakemake env
mamba create -n snakemake-8.5.3 -c conda-forge -c bioconda snakemake==8.5.3

# activate env
conda activate snakemake-8.5.3

# To make sure snakemake is working, print the help information
# for snakemake
snakemake --help

# If you want to make DAGs and rulegraphs, add GraphViz to this env
mamba install anaconda::graphviz

# change directories into Snakemake-Example
cd Snakemake-Example/

Initial Configuration of our work directory

  • We can use the Unix tree utility to see what the Snakemake-Example directory contains.
  • Within the Snakemake-Example directory, type tree at the command line. This shows:
    • A Snakefile. Much more about that later.
    • A directory data with three pairs of FASTQ files
    • A directory envs that has information to install necessary software with conda
    • A directory resources that has genome.fasta: a FASTA file with the reference genome
--% tree
.
├── Snakefile
├── data
│   ├── A_R1.fastq.gz
│   ├── A_R2.fastq.gz
│   ├── B_R1.fastq.gz
│   ├── B_R2.fastq.gz
│   ├── C_R1.fastq.gz
│   └── C_R2.fastq.gz
├── envs
│   ├── bcftools.yaml
│   ├── bwa2sam.yaml
│   ├── fastp.yaml
│   └── gatk.yaml
└── resources
    └── genome.fasta

4 directories, 12 files

How would you tackle this in a Unix way?

Consider the first two “steps”

flowchart TD
  H(fastq files from 3 samples: our raw data) --> I(Trim the reads: fastp)
  I --> J(Map the reads to a reference genome: bwa-mem2)

Some pseudo-shell code

# cycle over fastqs and do the trimming
for S in A B C; do
  fastp -i data/${S}_R1.fastq.gz -I data/S{S}_R2.fastq.gz \
    -o trimmed/${S}_R1.fastq.gz -O trimmed/${S}_R2.fastq.gz \
    other-arguments-etc...
done 


# cycle over trimmed fastqs and do the mapping
for S in A B C; do
  bwa-mem2 mem resources/genome.fasta \
    trimmed/${S}_R1.fastq.gz trimmed/${S}_R2.fastq.gz
done

What are some issues here?

  1. Ah crap! I forgot to index genome.fasta!
  2. This does not run the jobs in parallel!

Possible solutions for #2?

You can get things done in parallel using SLURM’s sbatch (which you probably need to use anyway).

Going about doing this with SLURM (a sketch…)

Consider the first two “steps”

flowchart TD
  H(fastq files from 3 samples: our raw data) --> I(Trim the reads: trimmomatic)
  I --> J(Map the reads to a reference genome: bwa mem)

Some pseudo-shell code

# cycle over fastqs and dispatch each trimming job to SLURM
for S in A B C; do
  sbatch my-trim-script.sh $S
done 

# ONCE ALL THE TRIMMING IS DONE...
# cycle over trimmed fastqs and dispatch each mapping job to SLURM
for S in A B C; do
  sbatch my-map-script $S
done

What is not-so-great about this?

  1. I have to wait for all the jobs of each step to finish
  2. I have to explicitly start each “next” step.
  3. If some jobs of a step fail, it is a PITA to go back and figure out which ones failed.
  4. The dependence between the outputs of the trimming step and the mapping step are implicit based on file paths buried in the scripts, rather than explicit.

The Advantages of Snakemake

  • The dependence between input and output files is explicit
  • This lets snakemake identify every single job that must be run—and the order they must be run in—for the entire workflow (all the steps)
  • This maximizes the number of jobs that can be run at once.
  • The necessary steps are determined by starting from the ultimate outputs that are desired or requested…
  • …then working backward through the dependencies to identify which jobs must be run to eventually get the ultimate output.
  • This greatly simplifies the problem of re-running any jobs that might have failed for reasons “known only to the cluster.”

Snakemake is a program that interprets a set of rules stored in a Snakefile

Some explanations:

  • Rule blocks: the fundamental unit
  • Correspond to “steps” in the workflow
  • Keyword “rule” + name + colon
  • Indenting like Python/YAML
  • Typically includes sub-blocks of input, output, and shell
  • (Also params, log, benchmarks, conda, etc.)
Snakefile
# run it over 3 samples
SAMPLES = ['A', 'B', 'C']

# variant calling over two "chromosomes"
CHROMOS = [ "NC_037122.1f5t9", "NC_037123.1f10t14"]


rule genome_faidx:
  input:
    "resources/genome.fasta",
  output:
    "resources/genome.fasta.fai",
  conda:
    "envs/bwa2sam.yaml"
  log:
    "results/logs/genome_faidx.log",
  shell:
    "samtools faidx {input} 2> {log} "


rule genome_dict:
  input:
    "resources/genome.fasta",
  output:
    "resources/genome.dict",
  conda:
    "envs/bwa2sam.yaml"
  log:
    "results/logs/genome_dict.log",
  shell:
    "samtools dict {input} > {output} 2> {log} "


rule bwa_index:
  input:
    "resources/genome.fasta"
  output:
    multiext("resources/genome.fasta", ".0123", ".amb", ".ann", ".bwt.2bit.64", ".pac"),
  conda:
    "envs/bwa2sam.yaml"
  log:
    out="results/logs/bwa_index/bwa_index.log",
    err="results/logs/bwa_index/bwa_index.err"
  shell:
    "bwa-mem2 index {input} > {log.out} 2> {log.err} "




rule trim_reads:
  input:
    r1="data/{sample}_R1.fastq.gz",
    r2="data/{sample}_R2.fastq.gz",
  output:
    r1="results/trimmed/{sample}_R1.fastq.gz",
    r2="results/trimmed/{sample}_R2.fastq.gz",
    html="results/qc/fastp/{sample}.html",
    json="results/qc/fastp/{sample}.json"
  conda:
    "envs/fastp.yaml"
  log:
    out="results/logs/trim_reads/{sample}.log",
    err="results/logs/trim_reads/{sample}.err",
  params:
    as1="AGATCGGAAGAGCACACGTCTGAACTCCAGTCA",
    as2="AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT",
    parm=" --detect_adapter_for_pe --cut_right --cut_right_window_size 4 --cut_right_mean_quality 20 "
  shell:
    " fastp -i {input.r1} -I {input.r2}       "
    "       -o {output.r1} -O {output.r2}     "
    "       -h {output.html} -j {output.json} "
    "  --adapter_sequence={params.as1}        "
    "  --adapter_sequence_r2={params.as2}     "
    "  {params.parm} > {log.out} 2> {log.err}                         "
    


rule map_reads:
  input:
    r1="results/trimmed/{sample}_R1.fastq.gz",
    r2="results/trimmed/{sample}_R2.fastq.gz",
    genome="resources/genome.fasta",
    idx=multiext("resources/genome.fasta", ".0123", ".amb", ".ann", ".bwt.2bit.64", ".pac")
  output:
    "results/bam/{sample}.bam"
  conda:
    "envs/bwa2sam.yaml"
  log:
    "results/logs/map_reads/{sample}.log"
  params:
    RG="-R '@RG\\tID:{sample}\\tSM:{sample}\\tPL:ILLUMINA' "
  shell:
    " (bwa-mem2 mem {params.RG} {input.genome} {input.r1} {input.r2} | "
    " samtools view -u | "
    " samtools sort - > {output}) 2> {log} "




rule mark_duplicates:
  input:
    "results/bam/{sample}.bam"
  output:
    bam="results/mkdup/{sample}.bam",
    bai="results/mkdup/{sample}.bai",
    metrics="results/qc/mkdup_metrics/{sample}.metrics"
  conda:
    "envs/gatk.yaml"
  log:
    "results/logs/mark_duplicates/{sample}.log"
  shell:
    " gatk MarkDuplicates  "
    "  --CREATE_INDEX "
    "  -I {input} "
    "  -O {output.bam} "
    "  -M {output.metrics} > {log} 2>&1 "




rule make_gvcfs_by_chromo:
  input:
    bam="results/mkdup/{sample}.bam",
    bai="results/mkdup/{sample}.bai",
    ref="resources/genome.fasta",
    idx="resources/genome.dict",
    fai="resources/genome.fasta.fai"
  output:
    gvcf="results/gvcf/{chromo}/{sample}.g.vcf.gz",
    idx="results/gvcf/{chromo}/{sample}.g.vcf.gz.tbi",
  conda:
    "envs/gatk.yaml"
  log:
    "results/logs/make_gvcfs_by_chromo/{chromo}/{sample}.log"
  params:
    java_opts="-Xmx4g"
  shell:
    " gatk --java-options \"{params.java_opts}\" HaplotypeCaller "
    " -R {input.ref} "
    " -I {input.bam} "
    " -O {output.gvcf} "
    " -L {wildcards.chromo}    "           
    " --native-pair-hmm-threads 1 " # this is just for this small example
    " -ERC GVCF > {log} 2> {log} "




rule import_genomics_db_by_chromo:
  input:
    gvcfs=expand("results/gvcf/{{chromo}}/{s}.g.vcf.gz", s=SAMPLES)
  output:
    gdb=directory("results/genomics_db/{chromo}")
  conda:
    "envs/gatk.yaml"
  log:
    "results/logs/import_genomics_db_by_chromo/{chromo}.log"
  params:
    java_opts="-Xmx4g"
  shell:
    " VS=$(for i in {input.gvcfs}; do echo -V $i; done); "  # make a string like -V file1 -V file2
    " gatk --java-options \"-Xmx4g\" GenomicsDBImport "
    "  $VS  "
    "  --genomicsdb-workspace-path {output.gdb} "
    "  -L  {wildcards.chromo} 2> {log} "




rule vcf_from_gdb_by_chromo:
  input:
    gdb="results/genomics_db/{chromo}",
    ref="resources/genome.fasta",
    fai="resources/genome.fasta.fai",
    idx="resources/genome.dict",
  output:
    vcf="results/chromo_vcfs/{chromo}.vcf.gz",
    idx="results/chromo_vcfs/{chromo}.vcf.gz.tbi",
  conda:
    "envs/gatk.yaml"
  log:
    "results/logs/vcf_from_gdb_by_chromo/{chromo}.txt"
  shell:
    " gatk --java-options \"-Xmx4g\" GenotypeGVCFs "
    "  -R {input.ref}  "
    "  -V gendb://{input.gdb} "
    "  -O {output.vcf} 2> {log} "


rule concat_vcfs:
  input:
    vcfs=expand("results/chromo_vcfs/{c}.vcf.gz", c=CHROMOS)
  output:
    vcf="results/vcf/all.vcf.gz"
  conda:
    "envs/bcftools.yaml"
  log:
    "results/concat_vcfs/all.log"
  shell:
    "bcftools concat -n {input.vcfs} > {output.vcf} 2> {log} "

A closer look at a simple rule

(Screen grab from Sublime Text which has great highlighting for Snakemake)

The rule:

  • Requires the input file resources/genome.fasta
  • Produces the output file resources/genome.dict
  • Uses software specified in envs/bwa2sam.yaml
  • Writes to a log file in results/logs/genome_dict.log
  • Executes the shell code samtools dict {input} > {output} 2> {log} to get the job done
  • What are those purple bits? {input}, {output}, and {log}?! in the shell code?
  • That is the syntax snakemake uses to substitute the values in the output, input, or log blocks (or other blocks…) into the Unix shell command.
  • Big Note: Output and log information is not written automatically to the output file and log file, nor is input taken automatically from the input file—you have to dicate that behavior by what you write in the shell block!
  • Thus, when this rule runs, the shell command executed will be:
samtools dict resources/genome.fasta > resources/genome.dict 2> results/logs/genome_dict.log

We “drive” Snakemake by requesting the creation of output files

These output files are sometimes referred to as “targets”

  • snakemake looks for and uses the Snakefile in the current working directory.
  • Option -n tells snakemake to do a “dry-run:” (Just say what you would do, but don’t do it!)
  • Option -p tells snakemake to print the shell commands of the rules.
  • Those two options can be combined: -np
  • And we request resources/genome.dict as a target by just putting it on the command line:
Paste this into your shell
snakemake -np resources/genome.dict
  • And the output you got from that should look like:
What the output should look like
Building DAG of jobs...
Job stats:
job            count
-----------  -------
genome_dict        1
total              1

Execute 1 jobs...

[Mon Feb 26 12:01:32 2024]
localrule genome_dict:
    input: resources/genome.fasta
    output: resources/genome.dict
    log: results/logs/genome_dict.log
    jobid: 0
    reason: Missing output files: resources/genome.dict
    resources: tmpdir=<TBD>

samtools dict resources/genome.fasta > resources/genome.dict 2> results/logs/genome_dict.log
Job stats:
job            count
-----------  -------
genome_dict        1
total              1

Reasons:
    (check individual jobs above for details)
    missing output files:
        genome_dict

This was a dry-run (flag -n). The order of jobs does not reflect the order of execution.

Direct snakemake to create all the conda environments to run this workflow

We are going to start by telling snakemake to create the conda environments needed to run the whole workflow:

Paste this into your shell
snakemake --conda-create-envs-only results/vcf/all.vcf.gz

The results/vcf/all.vcf.gz is the ultimate target of the workflow and the --conda-create-envs-only tells snakemake to do nothing more than create all the conda environments needed to create the output file results/vcf/all.vcf.gz.

The output snakemake gives you should look something like this:

Expected output of the above
Building DAG of jobs...
Your conda installation is not configured to use strict channel priorities. This is however crucial for having robust and correct environments (for details, see https://conda-forge.org/docs/user/tipsandtricks.html). Please consider to configure strict priorities by executing 'conda config --set channel_priority strict'.
Creating conda environment envs/bwa2sam.yaml...
Downloading and installing remote packages.
Environment for /Users/eriq/Documents/git-repos/con-gen-csu/Snakemake-Example/envs/bwa2sam.yaml created (location: .snakemake/conda/03ade215a4c713db728206723f154153_)
Creating conda environment envs/gatk.yaml...
Downloading and installing remote packages.
Environment for /Users/eriq/Documents/git-repos/con-gen-csu/Snakemake-Example/envs/gatk.yaml created (location: .snakemake/conda/d5b5e2cc0497b65f32514e037bc26ef9_)
Creating conda environment envs/bcftools.yaml...
Downloading and installing remote packages.
Environment for /Users/eriq/Documents/git-repos/con-gen-csu/Snakemake-Example/envs/bcftools.yaml created (location: .snakemake/conda/cbf79ac1a639509f529e96f71cf3c38b_)
Creating conda environment envs/fastp.yaml...
Downloading and installing remote packages.
Environment for /Users/eriq/Documents/git-repos/con-gen-csu/Snakemake-Example/envs/fastp.yaml created (location: .snakemake/conda/e37739b7128de56967cf47faf4c5cfef_)

Where do those environments get created? They do not go into your main conda library. Rather, as the output above shows, each environment gets stored inside .snakemake/conda in the current working directory.

A note on specifying conda environments for each rule

The genome_dict rule looks like this:

The genome_dict rule
rule genome_dict:
  input:
    "resources/genome.fasta",
  output:
    "resources/genome.dict",
  conda:
    "envs/bwa2sam.yaml"
  log:
    "results/logs/genome_dict.log",
  shell:
    "samtools dict {input} > {output} 2> {log} "

The conda: block tells it to use software defined in envs/bwa2sam.yaml, which looks like this:

Contents of envs/bwa2sam.yaml
channels:
  - bioconda
  - conda-forge
  - defaults
dependencies:
  - bwa-mem2 ==2.2.1
  - samtools ==1.19.2

Before it runs the genome_dict rule, Snakemake will make sure that a conda environment with bwa-mem2 and samtools is installed in the .snakemake/conda directory. If it is not, then it installs it.

Then it runs the rule. Note that software installation happens only once.

Now, let’s run the genome_dict rule!

  • Go back and do the dry-run for the genome_dict rule again:
Paste this into your shell
snakemake -np resources/genome.dict

Now, to do a real run (not a dry run) we remove the -n (dry-run) option, and, because we are having snakemake manage the needed software using conda, we add the --use-conda option, and we tell it to use 1 core for those run: --cores 1. So, our command now looks like:

Paste this into your shell
snakemake --use-conda --cores 1 resources/genome.dict
  • The output you get looks like what you saw before, but in this case the requested output file has been created.
  • And a log capturing stderr (if any) was created:
Paste this into your shell to see all the files
tree .

The output shows those two new files that were created

Output should look like this:
.
├── Snakefile
├── data
│   ├── A_R1.fastq.gz
│   ├── A_R2.fastq.gz
│   ├── B_R1.fastq.gz
│   ├── B_R2.fastq.gz
│   ├── C_R1.fastq.gz
│   └── C_R2.fastq.gz
├── envs
│   ├── bcftools.yaml
│   ├── bwa2sam.yaml
│   ├── fastp.yaml
│   └── gatk.yaml
├── resources
│   ├── genome.dict          <--- THIS IS A NEW FILE
│   └── genome.fasta
└── results
    └── logs
        └── genome_dict.log  <--- THIS IS A NEW FILE

6 directories, 14 files

Once a target file is created or updated Snakemake knows it

  • If you request the file resources/genome.dict from Snakemake now, it tells you that the file is there and does not need updating.
Paste this into your shell
snakemake -np resources/genome.dict
  • Because resources/genome.dict already exists (and none of its dependencies have been updated since it was created, and the code that creates the file in the rule has not been modified) Snakemake tells you this:
Expected output from Snakemake
Building DAG of jobs...
Nothing to be done (all requested files are present and up to date).
  • This helps you to not remake output files that don’t need remaking!

Wildcards: How Snakemake manages replication

Wildcards allow running multiple instances of the same rule on different input files by simple pattern matching

  • If we request from Snakemake the file
    results/trimmed/A_R1.fastq.gz,
  • then, Snakemake recognizes that this matches the output of rule trim_reads with the wildcard {sample} replaced by A.
  • And Snakemake propagates the value A of the wildcard {sample} to the input block.
  • Thus Snakemake knows that to create
    results/trimmed/A_R1.fastq.gz
    it needs the input files:
    • data/A_R1.fastq.gz
    • data/A_R2.fastq.gz

Try requesting those trimmed fastq files

  • See what snakemake would do when you ask for results/trimmed/A_R1.fastq.gz.
Paste this into your shell
snakemake -np results/trimmed/A_R1.fastq.gz
  • Note that you can request files from more than one sample:
Paste this into your shell
snakemake -np results/trimmed/A_R1.fastq.gz results/trimmed/B_R1.fastq.gz results/trimmed/C_R1.fastq.gz
  • Then, go ahead and run that last one, instructing Snakemake to use three cores, and using conda, as well!
Paste this into your shell
snakemake --cores 3 --use-conda results/trimmed/A_R1.fastq.gz results/trimmed/B_R1.fastq.gz results/trimmed/C_R1.fastq.gz

Note that it will go ahead and start all those jobs independently, and concurrently, because they do not depend on one another. This is how Snakemake manages and maximizes parallelism.

Important Notes I: Multiple wildcards can be used together

Here, chromo and sample are two different wildcards. And snakemake understands that a requested file with a path like:

results/gvcf/NC_037122.1f5t9/A.g.vcf.gz

matches the output file:

results/gvcf/{chromo}/{sample}.g.vcf.gz

with

chromo = NC_037122.1f5t9
sample = A

Important Notes II: expand()

Snakemake provides some functions that are useful for creating lists of input or output files. Note that, at the top of the Snakefile we define python lists SAMPLES and CHROMOS.

# run it over 3 samples
SAMPLES = ['A', 'B', 'C']

# variant calling over two "chromosomes"
CHROMOS = [ "NC_037122.1f5t9", "NC_037123.1f10t14"]

Then, later in the Snakefile, when we say:

rule import_genomics_db_by_chromo:
  input:
    gvcfs=expand("results/gvcf/{{chromo}}/{s}.g.vcf.gz", s=SAMPLES)

That expands the input files needed for rule import_genomics_db_by_chromo into the python list:

['results/gvcf/{chromo}/A.g.vcf.gz', 'results/gvcf/{chromo}/B.g.vcf.gz', 'results/gvcf/{chromo}/C.g.vcf.gz']

Note that the {s} in curly braces get expanded according to s=SAMPLES, and the {{chromo}} gets turned into {chromo}, so that it is interpreted as a wildcard in that file list. (In general you escape braces by doubling them.)

Important Notes III: multiext()

Snakemake provides the multiext() function which is like expand() but for file extensions.

Hence, when you see:

rule bwa_index:
  input:
    "resources/genome.fasta"
  output:
    multiext("resources/genome.fasta", ".0123", ".amb", ".ann", ".bwt.2bit.64", ".pac"),

This means that the output files from rule bwa_index are the python list:

[
'resources/genome.fasta.0123',
'resources/genome.fasta.amb',
'resources/genome.fasta.ann',
'resources/genome.fasta.bwt.2bit.64',
'resources/genome.fasta.pac'
]

Chains of file dependencies

  • If Snakemake does not find a required input file for a rule that provides a requested output, it searches through the outputs of all the other rules in the Snakefile to find a rule that might provide the required input file as one of its outputs.
  • It then schedules all the necessary rules to run.
  • This means that an entire workflow with thousands of jobs can be triggered by requesting a single output file.

Short Group Activity

  • Trace the rules needed if we request the file results/vcf/all.vcf.gz.
  • One person from each group, write down the rule dependencies.
  • Do this by finding the rule that creates results/vcf/all.vcf.gz as output, then finding the rules that would create the input for that rule, and so on and so forth, all the way back to the original fastq files.
Snakefile
# run it over 3 samples
SAMPLES = ['A', 'B', 'C']

# variant calling over two "chromosomes"
CHROMOS = [ "NC_037122.1f5t9", "NC_037123.1f10t14"]


rule genome_faidx:
  input:
    "resources/genome.fasta",
  output:
    "resources/genome.fasta.fai",
  conda:
    "envs/bwa2sam.yaml"
  log:
    "results/logs/genome_faidx.log",
  shell:
    "samtools faidx {input} 2> {log} "


rule genome_dict:
  input:
    "resources/genome.fasta",
  output:
    "resources/genome.dict",
  conda:
    "envs/bwa2sam.yaml"
  log:
    "results/logs/genome_dict.log",
  shell:
    "samtools dict {input} > {output} 2> {log} "


rule bwa_index:
  input:
    "resources/genome.fasta"
  output:
    multiext("resources/genome.fasta", ".0123", ".amb", ".ann", ".bwt.2bit.64", ".pac"),
  conda:
    "envs/bwa2sam.yaml"
  log:
    out="results/logs/bwa_index/bwa_index.log",
    err="results/logs/bwa_index/bwa_index.err"
  shell:
    "bwa-mem2 index {input} > {log.out} 2> {log.err} "




rule trim_reads:
  input:
    r1="data/{sample}_R1.fastq.gz",
    r2="data/{sample}_R2.fastq.gz",
  output:
    r1="results/trimmed/{sample}_R1.fastq.gz",
    r2="results/trimmed/{sample}_R2.fastq.gz",
    html="results/qc/fastp/{sample}.html",
    json="results/qc/fastp/{sample}.json"
  conda:
    "envs/fastp.yaml"
  log:
    out="results/logs/trim_reads/{sample}.log",
    err="results/logs/trim_reads/{sample}.err",
  params:
    as1="AGATCGGAAGAGCACACGTCTGAACTCCAGTCA",
    as2="AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT",
    parm=" --detect_adapter_for_pe --cut_right --cut_right_window_size 4 --cut_right_mean_quality 20 "
  shell:
    " fastp -i {input.r1} -I {input.r2}       "
    "       -o {output.r1} -O {output.r2}     "
    "       -h {output.html} -j {output.json} "
    "  --adapter_sequence={params.as1}        "
    "  --adapter_sequence_r2={params.as2}     "
    "  {params.parm} > {log.out} 2> {log.err}                         "
    


rule map_reads:
  input:
    r1="results/trimmed/{sample}_R1.fastq.gz",
    r2="results/trimmed/{sample}_R2.fastq.gz",
    genome="resources/genome.fasta",
    idx=multiext("resources/genome.fasta", ".0123", ".amb", ".ann", ".bwt.2bit.64", ".pac")
  output:
    "results/bam/{sample}.bam"
  conda:
    "envs/bwa2sam.yaml"
  log:
    "results/logs/map_reads/{sample}.log"
  params:
    RG="-R '@RG\\tID:{sample}\\tSM:{sample}\\tPL:ILLUMINA' "
  shell:
    " (bwa-mem2 mem {params.RG} {input.genome} {input.r1} {input.r2} | "
    " samtools view -u | "
    " samtools sort - > {output}) 2> {log} "




rule mark_duplicates:
  input:
    "results/bam/{sample}.bam"
  output:
    bam="results/mkdup/{sample}.bam",
    bai="results/mkdup/{sample}.bai",
    metrics="results/qc/mkdup_metrics/{sample}.metrics"
  conda:
    "envs/gatk.yaml"
  log:
    "results/logs/mark_duplicates/{sample}.log"
  shell:
    " gatk MarkDuplicates  "
    "  --CREATE_INDEX "
    "  -I {input} "
    "  -O {output.bam} "
    "  -M {output.metrics} > {log} 2>&1 "




rule make_gvcfs_by_chromo:
  input:
    bam="results/mkdup/{sample}.bam",
    bai="results/mkdup/{sample}.bai",
    ref="resources/genome.fasta",
    idx="resources/genome.dict",
    fai="resources/genome.fasta.fai"
  output:
    gvcf="results/gvcf/{chromo}/{sample}.g.vcf.gz",
    idx="results/gvcf/{chromo}/{sample}.g.vcf.gz.tbi",
  conda:
    "envs/gatk.yaml"
  log:
    "results/logs/make_gvcfs_by_chromo/{chromo}/{sample}.log"
  params:
    java_opts="-Xmx4g"
  shell:
    " gatk --java-options \"{params.java_opts}\" HaplotypeCaller "
    " -R {input.ref} "
    " -I {input.bam} "
    " -O {output.gvcf} "
    " -L {wildcards.chromo}    "           
    " --native-pair-hmm-threads 1 " # this is just for this small example
    " -ERC GVCF > {log} 2> {log} "




rule import_genomics_db_by_chromo:
  input:
    gvcfs=expand("results/gvcf/{{chromo}}/{s}.g.vcf.gz", s=SAMPLES)
  output:
    gdb=directory("results/genomics_db/{chromo}")
  conda:
    "envs/gatk.yaml"
  log:
    "results/logs/import_genomics_db_by_chromo/{chromo}.log"
  params:
    java_opts="-Xmx4g"
  shell:
    " VS=$(for i in {input.gvcfs}; do echo -V $i; done); "  # make a string like -V file1 -V file2
    " gatk --java-options \"-Xmx4g\" GenomicsDBImport "
    "  $VS  "
    "  --genomicsdb-workspace-path {output.gdb} "
    "  -L  {wildcards.chromo} 2> {log} "




rule vcf_from_gdb_by_chromo:
  input:
    gdb="results/genomics_db/{chromo}",
    ref="resources/genome.fasta",
    fai="resources/genome.fasta.fai",
    idx="resources/genome.dict",
  output:
    vcf="results/chromo_vcfs/{chromo}.vcf.gz",
    idx="results/chromo_vcfs/{chromo}.vcf.gz.tbi",
  conda:
    "envs/gatk.yaml"
  log:
    "results/logs/vcf_from_gdb_by_chromo/{chromo}.txt"
  shell:
    " gatk --java-options \"-Xmx4g\" GenotypeGVCFs "
    "  -R {input.ref}  "
    "  -V gendb://{input.gdb} "
    "  -O {output.vcf} 2> {log} "


rule concat_vcfs:
  input:
    vcfs=expand("results/chromo_vcfs/{c}.vcf.gz", c=CHROMOS)
  output:
    vcf="results/vcf/all.vcf.gz"
  conda:
    "envs/bcftools.yaml"
  log:
    "results/concat_vcfs/all.log"
  shell:
    "bcftools concat -n {input.vcfs} > {output.vcf} 2> {log} "

Let’s request results/vcf/all.vcf from Snakemake

  • Let’s start with a dry run:
Paste this into your shell
snakemake -np results/vcf/all.vcf.gz
  • After we look at that, and discuss, let’s actually run it, using 4 cores:
Paste this into your shell
snakemake -p --cores 4 results/vcf/all.vcf.gz

That should take a minute or three.

  • If you try to run the workflow again, Snakemake tells you that you do not need to, because everything is up to date: Try running the above line again:
Paste this into your shell
snakemake -p --use-conda --cores 2 results/vcf/all.vcf.gz

If any inputs change, Snakemake will re-run the rules that depend on the new input

  • Imagine that the sequencing center calls us to say that there has been a terrible mistake and they are sending you new (and correct) versions of data for sample C: C_R1.fastq.gz and C_R2.fastq.gz
  • Snakemake uses file modification dates to check if any inputs have been updated after target outputs have been created.
  • So we can simulate new fastq files for sample C by using the touch command to update the fastq file modification dates:
Paste this into your shell
touch data/C_R1.fastq.gz data/C_R2.fastq.gz
  • Now, when we run Snakemake again, it tells us we have to run more jobs, but only the ones that depend on data from sample C. Do a dry run to check that:
Paste this into your shell
snakemake -np results/vcf/all.vcf.gz
  • Check that it will not re-run the trimming, mapping, and gvcf-making steps for samples A and B, which are already done.

Snakemake makes it very easy to re-run failed jobs

  • Clusters and computers fail (sometimes for no apparent reason) occasionally
  • If this happens in a large, traditionally managed (Unix script) workflow, finding and re-running the failures can be hard.
  • Example: 7 birds out of 192 fail on HaplotypeCaller because those jobs got sent to nodes without AVX acceleration.
  • Five years ago, setting up custom scripts to re-run just those 7 birds could cost me an hour—about as much time as it takes me now to set up an entire workflow with Snakemake.
  • On the next slide we are going to create a job failure to see how easy it is to re-run jobs that failed with Snakemake.

Simulating a job failure as an example

  • First, let’s remove the entire results directory, so that we have to re-run most of our workflow.
Paste this into your shell
rm -rf results
  • Now, we are going to corrupt the read-2 fastq file for sample A (but keeping a copy of the original)
Paste this into your shell
cp data/A_R2.fastq.gz data/A_R2.fastq.gz-ORIG
echo "GARBAGE_DATA" | gzip -c > data/A_R2.fastq.gz
  • Now, do a dry-run, requesting results/vcf/all.vcf.gz
Paste this into your shell
snakemake -np results/vcf/all.vcf.gz

The output ends telling us that 20 jobs will be run:

End of the expected output
Job stats:
job                             count
----------------------------  -------
concat_vcfs                         1
import_genomics_db_by_chromo        2
make_gvcfs_by_chromo                6
map_reads                           3
mark_duplicates                     3
trim_reads                          3
vcf_from_gdb_by_chromo              2
total                              20
  • Now, run it with 4 cores and give it the --keep-going command which means that even if an error occurs on one job, all the other jobs that don’t depend on outputs from the failed job will still get run.
Paste this into your shell
snakemake --cores 4 --use-conda --keep-going results/vcf/all.vcf.gz
  • Snakemake runs as far as it can and then wraps it up, telling us that 8 of the 14 jobs were successful but at least one job failed:
Snakemake's concluding comments:
10 of 20 steps (50%) done
Exiting because a job execution failed. Look above for error message
Complete log: .snakemake/log/2024-02-26T161639.704830.snakemake.log
WorkflowError:
At least one job did not complete successfully.

Here is a related, fun tip: Snakemake writes the log of every real (i.e., without the -n option) run into a log file in .snakemake/log. Try this:

ls .snakemake/log

If you want to get the log from the most recent run you can throw down some Unix:

ls -l .snakemake/log/*.log | tail -n 1 | awk '{print $NF}'

You can use that to find any lines that say Error in rule in them (and 10 or 11 lines after it says “Error in rule”) to tell you more about the job that failed. e.g.:

grep "Error in rule"  -A 11 $(ls -l .snakemake/log/*.log | tail -n 1 | awk '{print $NF}')

Re-running failed jobs is as simple as just re-starting Snakemake

  • First off, after a failure like that, we can always immediately do a dry-run to see what Snakemake must still do to finish out the workflow:
Paste this into your shell
snakemake -np results/vcf/all.vcf.gz

It is only going to require 10 more jobs to produce results/vcf/all.vcf:

This is the end of the dry-run output
Job stats:
job                             count
----------------------------  -------
concat_vcfs                         1
import_genomics_db_by_chromo        2
make_gvcfs_by_chromo                2
map_reads                           1
mark_duplicates                     1
trim_reads                          1
vcf_from_gdb_by_chromo              2
total                              10

  • We see that it still has to do 1 trim job, which is the one that failed.

  • If we noticed that data/A_R2.fastq.gz was corrupted, we could replace it with the uncorrupted version:

Paste this into your shell
cp data/A_R2.fastq.gz-ORIG data/A_R2.fastq.gz
  • Then, start it back up with 4 cores:
Paste this into your shell
snakemake --cores 4 --use-conda results/vcf/all.vcf.gz

Now that sample A is not corrupted, it finishes. Yay! That was easy.

Snakemake encourages (requires?) that your outputs all reside in a consistent directory structure

(And a side note: Snakemake automatically creates all the directories needed to store its output files)

  • Check out all the outputs of our workflow in an easy-to-understand directory structure within results:
Paste this into your shell
# only drill down three directory levels (-L 3)
tree -L 3 results

Here is what the result looks like:

The tree listing of the full results of the workflow
results
├── bam
│   ├── A.bam
│   ├── B.bam
│   └── C.bam
├── chromo_vcfs
│   ├── NC_037122.1f5t9.vcf.gz
│   ├── NC_037122.1f5t9.vcf.gz.tbi
│   ├── NC_037123.1f10t14.vcf.gz
│   └── NC_037123.1f10t14.vcf.gz.tbi
├── concat_vcfs
│   └── all.log
├── genomics_db
│   ├── NC_037122.1f5t9
│   │   ├── NC_037122.1f5t9$1$4000001
│   │   ├── __tiledb_workspace.tdb
│   │   ├── callset.json
│   │   ├── vcfheader.vcf
│   │   └── vidmap.json
│   └── NC_037123.1f10t14
│       ├── NC_037123.1f10t14$1$4000001
│       ├── __tiledb_workspace.tdb
│       ├── callset.json
│       ├── vcfheader.vcf
│       └── vidmap.json
├── gvcf
│   ├── NC_037122.1f5t9
│   │   ├── A.g.vcf.gz
│   │   ├── A.g.vcf.gz.tbi
│   │   ├── B.g.vcf.gz
│   │   ├── B.g.vcf.gz.tbi
│   │   ├── C.g.vcf.gz
│   │   └── C.g.vcf.gz.tbi
│   └── NC_037123.1f10t14
│       ├── A.g.vcf.gz
│       ├── A.g.vcf.gz.tbi
│       ├── B.g.vcf.gz
│       ├── B.g.vcf.gz.tbi
│       ├── C.g.vcf.gz
│       └── C.g.vcf.gz.tbi
├── logs
│   ├── import_genomics_db_by_chromo
│   │   ├── NC_037122.1f5t9.log
│   │   └── NC_037123.1f10t14.log
│   ├── make_gvcfs_by_chromo
│   │   ├── NC_037122.1f5t9
│   │   └── NC_037123.1f10t14
│   ├── map_reads
│   │   ├── A.log
│   │   ├── B.log
│   │   └── C.log
│   ├── mark_duplicates
│   │   ├── A.log
│   │   ├── B.log
│   │   └── C.log
│   ├── trim_reads
│   │   ├── A.err
│   │   ├── A.log
│   │   ├── B.err
│   │   ├── B.log
│   │   ├── C.err
│   │   └── C.log
│   └── vcf_from_gdb_by_chromo
│       ├── NC_037122.1f5t9.txt
│       └── NC_037123.1f10t14.txt
├── mkdup
│   ├── A.bai
│   ├── A.bam
│   ├── B.bai
│   ├── B.bam
│   ├── C.bai
│   └── C.bam
├── qc
│   ├── fastp
│   │   ├── A.html
│   │   ├── A.json
│   │   ├── B.html
│   │   ├── B.json
│   │   ├── C.html
│   │   └── C.json
│   └── mkdup_metrics
│       ├── A.metrics
│       ├── B.metrics
│       └── C.metrics
├── trimmed
│   ├── A_R1.fastq.gz
│   ├── A_R2.fastq.gz
│   ├── B_R1.fastq.gz
│   ├── B_R2.fastq.gz
│   ├── C_R1.fastq.gz
│   └── C_R2.fastq.gz
└── vcf
    └── all.vcf.gz

Snakemake eye-candy—visualizing the workflow dependencies

Using the --dag option, like this:

Paste this into your shell
snakemake --dag results/vcf/all.vcf.gz | dot -Tsvg > dag.svg

Makes a directed acyclic graph (DAG) of the workflow. If you view it, it looks like this:

snakemake_dag 0 concat_vcfs 1 vcf_from_gdb_by_chromo 1->0 2 import_genomics_db_by_chromo 2->1 3 make_gvcfs_by_chromo chromo: NC_037122.1f5t9 3->2 4 mark_duplicates 4->3 20 make_gvcfs_by_chromo chromo: NC_037123.1f10t14 4->20 5 map_reads 5->4 6 trim_reads sample: A 6->5 7 bwa_index 7->5 12 map_reads 7->12 16 map_reads 7->16 8 genome_dict 8->1 8->3 10 make_gvcfs_by_chromo chromo: NC_037122.1f5t9 8->10 14 make_gvcfs_by_chromo chromo: NC_037122.1f5t9 8->14 18 vcf_from_gdb_by_chromo 8->18 8->20 21 make_gvcfs_by_chromo chromo: NC_037123.1f10t14 8->21 22 make_gvcfs_by_chromo chromo: NC_037123.1f10t14 8->22 9 genome_faidx 9->1 9->3 9->10 9->14 9->18 9->20 9->21 9->22 10->2 11 mark_duplicates 11->10 11->21 12->11 13 trim_reads sample: B 13->12 14->2 15 mark_duplicates 15->14 15->22 16->15 17 trim_reads sample: C 17->16 18->0 19 import_genomics_db_by_chromo 19->18 20->19 21->19 22->19

Snakemake eye-candy—filegraphs

Using the --filegraph option, like this:

Paste this into your shell
snakemake --filegraph results/vcf/all.vcf.gz | dot -Tsvg > filegraph.svg

Makes a graph (DAG) of the files involved in the workflow. If you view it, it looks like the following, which is exactly the sort of figure you would have created had you been extremely diligent about the tracing all the dependencies in the Snakefile with you team:

snakemake_dag 0 concat_vcfs ↪ input results/chromo_vcfs/NC_037122.1f5t9.vcf.gz results/chromo_vcfs/NC_037123.1f10t14.vcf.gz output → results/vcf/all.vcf.gz 1 vcf_from_gdb_by_chromo ↪ input resources/genome.dict resources/genome.fasta resources/genome.fasta.fai results/genomics_db/{chromo} output → results/chromo_vcfs/{chromo}.vcf.gz results/chromo_vcfs/{chromo}.vcf.gz.tbi 1->0 2 import_genomics_db_by_chromo ↪ input results/gvcf/{chromo}/A.g.vcf.gz results/gvcf/{chromo}/B.g.vcf.gz results/gvcf/{chromo}/C.g.vcf.gz output → results/genomics_db/{chromo} 2->1 3 make_gvcfs_by_chromo ↪ input resources/genome.dict resources/genome.fasta resources/genome.fasta.fai results/mkdup/{sample}.bai results/mkdup/{sample}.bam output → results/gvcf/{chromo}/{sample}.g.vcf.gz results/gvcf/{chromo}/{sample}.g.vcf.gz.tbi 3->2 4 mark_duplicates ↪ input results/bam/{sample}.bam output → results/mkdup/{sample}.bai results/mkdup/{sample}.bam results/qc/mkdup_metrics/{sample}.metrics 4->3 5 map_reads ↪ input resources/genome.fasta resources/genome.fasta.0123 resources/genome.fasta.amb resources/genome.fasta.ann resources/genome.fasta.bwt.2bit.64 resources/genome.fasta.pac results/trimmed/{sample}_R1.fastq.gz results/trimmed/{sample}_R2.fastq.gz output → results/bam/{sample}.bam 5->4 6 trim_reads ↪ input data/{sample}_R1.fastq.gz data/{sample}_R2.fastq.gz output → results/qc/fastp/{sample}.html results/qc/fastp/{sample}.json results/trimmed/{sample}_R1.fastq.gz results/trimmed/{sample}_R2.fastq.gz 6->5 7 bwa_index ↪ input resources/genome.fasta output → resources/genome.fasta.0123 resources/genome.fasta.amb resources/genome.fasta.ann resources/genome.fasta.bwt.2bit.64 resources/genome.fasta.pac 7->5 8 genome_dict ↪ input resources/genome.fasta output → resources/genome.dict 8->1 8->3 9 genome_faidx ↪ input resources/genome.fasta output → resources/genome.fasta.fai 9->1 9->3

Here is a more complex workflow graph

The dag for a run of mega-non-model-wgs-snakeflow that was processed by my R package SnakemakeDagR

snakemake_dag 1 bcf_concat: 1 37 all: 1 1->37 1 2 bcf_concat_mafs: 1 2->37 1 3 bcf_maf_section_summaries: 32 9 combine_maf_bcftools_stats: 1 3->9 32 4 bcf_section_summaries: 96 [filter_condition] 8 combine_bcftools_stats: 3 4->8 96 5 bung_filtered_vcfs_back_together: 32 5->1 32 5->4 96 24 maf_filter: 32 [maf] 5->24 32 6 bwa_index: 1 31 map_reads: 43 6->31 43 7 clip_overlaps: 43 7->37 43 8->37 3 9->37 1 10 concat_gvcf_sections: 43 10->37 43 11 fastqc_read1: 43 [bqsr_round, sample, unit] 34 multiqc_dir: 1 11->34 43 12 fastqc_read2: 43 [bqsr_round, sample, unit] 12->34 43 13 gather_scattered_vcfs: 32 32 mark_dp0_as_missing: 32 13->32 32 14 genome_dict: 1 26 make_gvcf_sections: 1376 [sg_or_chrom] 14->26 1376 15 genome_faidx: 1 21 get_genome_length: 1 [bqsr_round] 15->21 1 15->26 1376 16 genomics_db2vcf_scattered: 240 16->13 240 17 genomics_db_import_chromosomes: 31 [chromo] 17->16 176 18 genomics_db_import_scaffold_groups: 1 [scaff_group] 18->16 64 19 get_ave_depths: 1 19->37 1 20 get_genome: 1 20->6 1 20->14 1 20->15 1 20->16 240 20->26 1376 21->19 1 22 hard_filter_indels: 32 22->5 32 23 hard_filter_snps: 32 23->5 32 24->2 32 24->3 32 25 make_chromo_interval_lists: 31 [bqsr_round, chromo] 25->26 1333 26->10 1376 26->17 1333 26->18 43 27 make_indel_vcf: 32 27->22 32 28 make_scaff_group_interval_lists: 1 [bqsr_round, scaff_group] 28->26 43 29 make_scatter_interval_lists: 240 [bqsr_round, scatter, sg_or_chrom] 29->16 240 30 make_snp_vcf: 32 30->23 32 33 mark_duplicates: 43 31->33 43 32->27 32 32->30 32 33->7 43 33->26 1376 33->34 43 35 samtools_stats: 43 33->35 43 34->37 1 35->19 43 35->34 43 36 trim_reads_pe: 43 [bqsr_round, sample, unit] 36->31 43 36->34 43

We’ve only scratched the surface

  • Python code is allowed in most places in the Snakefile
  • Input functions can be quite useful (or absolutely essential)
  • You can benchmark every job instance of a rule, which records the resources used (time, memory, etc.)

Benchmarking jobs

This is super easy. You add to each rule a path for a benchmark file. For example:

rule map_reads:
  input:
    r1="results/trimmed/{sample}_R1.fastq.gz",
    r2="results/trimmed/{sample}_R2.fastq.gz",
    genome="resources/genome.fasta",
    idx=multiext("resources/genome.fasta", ".0123", ".amb", ".ann", ".bwt.2bit.64", ".pac")
  output:
    "results/bam/{sample}.bam"
  conda:
    "envs/bwa2sam.yaml"
  log:
    "results/logs/map_reads/{sample}.log"
  benchmark:
    "results/benchmarks/map_reads/{sample}.bmk"
  params:
    RG="-R '@RG\\tID:{sample}\\tSM:{sample}\\tPL:ILLUMINA' "
  shell:
    " (bwa-mem2 mem {params.RG} {input.genome} {input.r1} {input.r2} | "
    " samtools view -u | "
    " samtools sort - > {output}) 2> {log} "

Then, each time that rule gets run (with a different wildcard) you get a little file like this:

s   h:m:s   max_rss max_vms max_uss max_pss io_in   io_out  mean_load   cpu_time
164.7159    0:02:44 293.05  3523.55 263.11  269.15  1740.09 0.12    76.83   127.05
  • This tells you how many seconds it ran (s), the max RAM use (max_rss), amount of data read from disk and written to disk, total amount of CPU time, etc.
  • Super helpful
  • Easy to extract

Benchmark Example

Jobs processing 43 rockfish on NMFS on-premises cluster (SEDNA) vs in the cloud on Microsoft Azure (AZHOP)

Benchmark Example

Super easy to extract that for all the rules with a little Unix and R

Where to from here?

You might be interested in having a look at a workflow I wrote for whole genome sequencing of non-model organisms: https://github.com/eriqande/mega-non-model-wgs-snakeflow.

This provides a complete BWA-GATK workflow including an arbitrary number of “bootstrapped-BQSR” rounds (which turn out to be pretty useless…)

Letting Snakemake interface with SLURM

The way to most easily allow Snakemake to dispatch jobs via the SLURM scheduler is by way of the Snakemake cluster option provided in a Snakemake profile.

A Snakemake profile is a YAML file in which you can record command line options (and their arguments) for Snakemake.

There is an officially supported Snakemake profile for SLURM, but I am partial to the Unix-based (as opposed to Python-based) approach to SLURM profiles for Snakemake described at: https://github.com/jdblischak/smk-simple-slurm.

A Snakemake profile for SLURM on Alpine

Alpine is the new NSF-funded cluster in Colorado

  • A Snakemake profile is simply a collection of command line arguments stored in a YAML file.

  • The set-resources and threads blocks are specific to my lcWGS workflow

Contents of hpcc-profiles/slurm/alpine/config.yaml
cluster:
  mkdir -p results/slurm_logs/{rule} &&
  sbatch
    --partition=amilan,csu
    --cpus-per-task={threads}
    --mem={resources.mem_mb}
    --time={resources.time}
    --job-name=smk-{rule}-{wildcards}
    --output=results/slurm_logs/{rule}/{rule}-{wildcards}-%j.out
    --error=results/slurm_logs/{rule}/{rule}-{wildcards}-%j.err
    --parsable
default-resources:
  - time="08:00:00"
  - mem_mb=3740
  - tmpdir="results/snake-tmp"
restart-times: 0
max-jobs-per-second: 10
max-status-checks-per-second: 50
local-cores: 1
latency-wait: 60
cores: 2400
jobs: 950
keep-going: True
rerun-incomplete: True
printshellcmds: True
use-conda: True
rerun-trigger: mtime
cluster-status: status-sacct-robust.sh
cluster-cancel: scancel
cluster-cancel-nargs: 4000


set-threads:
  map_reads: 4
  realigner_target_creator: 4
  genomics_db_import_chromosomes: 2
  genomics_db_import_scaffold_groups: 2
  genomics_db2vcf_scattered: 2
set-resources:
  map_reads:
    mem_mb: 14960
    time: "23:59:59"
  make_gvcf_sections:
    mem_mb: 3600
    time: "23:59:59"
  genomics_db_import_chromosomes:
    mem_mb: 7480
    time: "23:59:59"
  genomics_db_import_scaffold_groups:
    mem_mb: 11000
    time: "23:59:59"
  genomics_db2vcf_scattered:
    mem_mb: 11000
    time: "23:59:59"
  multiqc_dir:
    mem_mb: 37000
  bwa_index:
    mem_mb: 37000
  realigner_target_creator:
    mem_mb: 14960

Setting this up for yourself

To use Snakemake on your own server/cluster/laptop you should:

  1. Install Mambaforge as described here
  2. Create a new conda environment for snakemake as described here
# i.e.:
mamba create -c conda-forge -c bioconda -n snakemake snakemake
  1. Then:
conda activate snakemake

Some notes: mamba is far faster (and better in almost all cases) than conda. It is also practically required for using snakemake conda blocks.

Final Thoughts

  • Learning snakemake may require a bit of an investment, BUT…
  • For anyone doing a lot of bioinformatic processing of sequence data it is quite a sound investment.

One final step on the command line:

conda deactivate